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Segmentation of computer tomography image using local robust statistics and region-scalable fitting

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成果类型:
期刊论文
作者:
Li, Linsheng;Zeng, Li*;Qiu, Changjun;Liu, Linghui
通讯作者:
Zeng, Li
作者机构:
[Li, Linsheng; Zeng, Li; Liu, Linghui] Chongqing Univ, Educ Minist China, Key Lab Optoelect Technol & Syst, ICT Res Ctr, Chongqing 400044, Peoples R China.
[Li, Linsheng; Qiu, Changjun] Univ S China, Coll Mech Engn, Hengyang, Hunan, Peoples R China.
[Zeng, Li] Chongqing Univ, Coll Math & Stat, Chongqing 400044, Peoples R China.
通讯机构:
[Zeng, Li] C
Chongqing Univ, Educ Minist China, Key Lab Optoelect Technol & Syst, ICT Res Ctr, Chongqing 400044, Peoples R China.
语种:
英文
关键词:
Image segmentation;intensity inhomogeneity;computer tomography (CT);robust statistics;region-scalable fitting (RSF)
期刊:
Journal of X-Ray Science and Technology
ISSN:
0895-3996
年:
2012
卷:
20
期:
3
页码:
255-267
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [60972104, 50974075]
机构署名:
本校为其他机构
院系归属:
机械工程学院
摘要:
Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted comb...

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